The cervical smear (Pap test) is the only mass screening cytological examination which requires visual inspection of virtually every cell on the slide. The test suffers from a high false negative rate due to the tedium and fatigue associated with its current manual mode of performance. Cell classification is typically performed on a "piece-work" basis by "cytotechnicians" employed by pathology laboratories and in some circumstances by salaried technicians. Due to the clearly life threatening nature of the false negative problem with its resultant possibility of undiagnosed cervical cancer, the American Cancer Society is considering doubling the frequency of recommended Pap smears. This, however, will certainly overload an already overburdened cervical screening industry as increasingly fewer individuals are willing to enter the tedious and stressful field of manual cervical smear classification. An American Cancer Society recommendation to increase Pap smear frequency may only serve to increase the false negative rate by decreasing the amount of time spent on manual examination of each slide. A thorough manual examination should take no less than fifteen minutes per slide although a cytotechnician, especially one under a heavy workload, may spend less than half this amount of time. The College of American Pathology is well aware of this problem and would rapidly embrace an automated solution to cervical smear screening.
Due to the clear commercial potential for automated cervical smear analysis several attempts to this end have been made in the prior art. These attempts have proven to be unsuccessful since they have relied exclusively on classical pattern recognition technology (geometric, syntactic, template, statistical) or artificial intelligence (AI) based pattern recognition, i.e., rule-based expert systems. There is, however, no clear algorithm or complete and explicit set of rules by which the human cytotechnician or pathologist uses his experience to combine a multitude of features to make a classification in gestalt manner. Cervical smear classification is, therefore, an excellent application for neural network based pattern recognition.
An example of the limitations of the prior art can be found in the 1987 reference entitled "Automated Cervical Screen Classification" by Tien et al, identified further below.
Background references of interest are, as follows:
Rumelhart, David E. and McClelland, James L., "Parallel Distributed Processing," MIT Press, 1986, Volume 1;
Tien, D. et al, "Automated Cervical Smear Classification," Proceedings of the IEEE/Ninth Annual Conference of the Engineering in Medicine and Biology Society, 1987, p. 1457-1458;
Hecht-Nielsen, Robert, "Neurocomputing: Picking the Human Brain," IEEE Spectrum, March, 1988, p. 36-41; and
Lippmann, Richard P., "An Introduction to Computing with Neural Nets," IEEE ASSP Magazine, April, 1987, p. 4-22.